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experimental.to() function | The `experimental.to()` function writes data to an InfluxDB v2.0 bucket. The function structures data differently than the built-in `to()` function. |
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The experimental.to()
function writes data to an InfluxDB v2.0 bucket, but in
a different structure than the
built-in to()
function.
Function type: Output
import "experimental"
experimental.to(
bucket: "my-bucket",
org: "my-org"
)
// OR
experimental.to(
bucketID: "1234567890",
orgID: "0987654321"
)
Expected data structure
Data structure expected by built-in to()
The built-in to()
function requires _time
, _measurement
, _field
, and _value
columns.
The _field
column stores the field key and the _value
column stores the field value.
_time | _measurement | _field | _value |
---|---|---|---|
timestamp | measurement-name | field key | field value |
Data structure expected by experimental to()
experimental.to()
requires _time
and measurement
columns, but field keys
and values are stored in single columns with the field key as the column name and
the field value as the column value.
_time | _measurement | field_key |
---|---|---|
timestamp | measurement-name | field value |
If using the built-in from()
function, use pivot()
to transform data into the structure experimetnal.to()
expects.
See the example below.
Parameters
bucket
The bucket to write data to.
bucket
and bucketID
are mutually exclusive.
Data type: String
bucketID
The ID of the bucket to write data to.
bucketID
and bucket
are mutually exclusive.
Data type: String
org
The organization name of the specified bucket
.
Only required when writing to a different organization or a remote host.
org
and orgID
are mutually exclusive.
Data type: String
orgID
The organization ID of the specified bucket
.
Only required when writing to a different organization or a remote host.
orgID
and org
are mutually exclusive.
Data type: String
Examples
Use pivot() to shape data for experimental.to()
import "experimental"
from(bucket: "example-bucket")
|> range(start: -1h)
|> pivot(
rowKey:["_time"],
columnKey: ["_field"],
valueColumn: "_value")
|> experimental.to(
bucket: "bucket-name",
org: "org-name"
)